Point clouds are one special representation of objects in 3D space.
The recent availability of large range 3D scanner devices has led to the research and development in many areas of point cloud processing [1]}.
Classically, large amounts of handcrafted features were extracted from the point clouds (e.g., eigenvalues or entropy), making sophisticated neighborhood and feature selection necessary. Although in the area of deep learning, there has not been much done in the early years.
But since the adaption of PointNet [2]} and PointNet++ [3]} much work has been done on point clouds.
These deep networks are trained to alleviate the process of feature engineering and selection and have done very well in the area of object classification and segmentation.
The feature extraction and classification accuracy is further improved with new architectures like Graph-CNNs [4]} using graph convolution and Dynamic Graph-CNNs [5]} which also extracts edge features using edge convolution.
